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Advances and Applications of Complex Data Analysis and Computing
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Advances and Applications of Complex Data Analysis and Computing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 March 2025 | Viewed by 4675

Special Issue Editors

Prof. Dr. Lykourgos Magafas

E-Mail Website
Guest Editor
1. Laboratory of Complex Systems, Faculty of Sciences, Democrats University of Thrace, 65404 Kavala, Greece
2. Department of Physics, Faculty of Sciences, Democrats University of Thrace, 65404 Kavala, Greece
Interests: dynamic systems and chaos; network theory; complex artificial intelligence systems; complex data analysis and computing
Special Issues, Collections and Topics in MDPI journals
Dr. Rui Araújo

E-Mail Website
Guest Editor
Institute of Systems and Robotics (ISR-UC), and Department of Electrical and Computer Engineering (DEEC-UC), University of Coimbra, Pólo II, PT-3030-290 Coimbra, Portugal
Interests: computational intelligence; intelligent control; computational learning; machine learning; fuzzy systems; neural networks; optimization; modeling; simulation; estimation; prediction; control; big data; robotics; mobile robotics; intelligent vehicles; robot manipulators control; sensing; soft sensors; automation; industrial systems; embedded systems; real-time systems; in general architectures; systems for controlling robot manipulators; mobile robots
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The following areas of interest have been selected as relevant to this Special Issue:

  1. Information and Communication Technologies (the Internet, WWW, Searches, the Semantic web, Blockchain, and Bitcoin).
  2. Information Processing in Cognition, Intelligence and Learning (natural and artificial), Neurosciences, and Linguistics.
  3. Econometrics, Finance and Game Theory.
  4. Infrastructure, Logistics, Transport, Energy and Smart Cities.
  5. Biological and Medical Data.
  6. Social Complexity, Social Communities and Networks, Social Evolution, Epidemics and Contagions.
  7. Ecological Systems, Global Environmental Change, Green Growth, Sustainability and Resilience.
  8. Complexity in Physics and Chemistry.
  9. Quantum Computation, Synchronization, and Chaos.
  10. Classical and Quantum Cryptography.

Prof. Dr. Lykourgos Magafas
Dr. Rui Araújo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • complex time series
  • physics chemistry computation
  • classical and quantum cryptography
  • classical and quantum intelligence and learning (natural and artificial)

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Published Papers (4 papers)

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Research

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19 pages, 5530 KiB  
Article
TopoSinGAN: Learning a Topology-Aware Generative Model from a Single Image
by Mohsen Ahmadkhani and Eric Shook
Appl. Sci. 2024, 14(21), 9944; https://doi.org/10.3390/app14219944 - 30 Oct 2024
Viewed by 561
Abstract
Generative adversarial networks (GANs) have significantly advanced synthetic image generation, yet ensuring topological coherence remains a challenge. This paper introduces TopoSinGAN, a topology-aware extension of the SinGAN framework, designed to enhance the topological accuracy of generated images. TopoSinGAN incorporates a novel, differentiable topology [...] Read more.
Generative adversarial networks (GANs) have significantly advanced synthetic image generation, yet ensuring topological coherence remains a challenge. This paper introduces TopoSinGAN, a topology-aware extension of the SinGAN framework, designed to enhance the topological accuracy of generated images. TopoSinGAN incorporates a novel, differentiable topology loss function that minimizes terminal node counts along predicted segmentation boundaries, thereby addressing topological anomalies not captured by traditional losses. We evaluate TopoSinGAN using agricultural and dendrological case studies, demonstrating its capability to maintain boundary continuity and reduce undesired loop openness. A novel evaluation metric, Node Topology Clustering (NTC), is proposed to assess topological attributes independently of geometric variations. TopoSinGAN significantly improves topological accuracy, reducing NTC index values from 15.15 to 3.94 for agriculture and 14.55 to 2.44 for dendrology, compared to the baseline SinGAN. Modified FID evaluations also show improved realism, with lower FID scores: 0.1914 for agricultural fields compared to 0.2485 for SinGAN, and 0.0013 versus 0.0014 for dendrology. The topology loss enables end-to-end training with direct topological feedback. This new framework advances the generation of topologically accurate synthetic images, with applications in fields requiring precise structural representations, such as geographic information systems (GIS) and medical imaging. Full article
(This article belongs to the Special Issue Advances and Applications of Complex Data Analysis and Computing)
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31 pages, 5711 KiB  
Article
Self-Adaptable Software for Pre-Programmed Internet Tasks: Enhancing Reliability and Efficiency
by Mario Martínez García, Luis Carlos G. Martínez Rodríguez and Ricardo Pérez Zúñiga
Appl. Sci. 2024, 14(15), 6827; https://doi.org/10.3390/app14156827 - 5 Aug 2024
Viewed by 719
Abstract
In the current digital landscape, artificial intelligence-driven automation has revolutionized efficiency in various areas, enabling significant time and resource savings. However, the reliability and efficiency of software systems remain crucial challenges. To address this issue, a generation of self-adaptive software has emerged with [...] Read more.
In the current digital landscape, artificial intelligence-driven automation has revolutionized efficiency in various areas, enabling significant time and resource savings. However, the reliability and efficiency of software systems remain crucial challenges. To address this issue, a generation of self-adaptive software has emerged with the ability to rectify errors and autonomously optimize performance. This study focuses on the development of self-adaptive software designed for pre-programmed tasks on the Internet. The software stands out for its self-adaptation, automation, fault tolerance, efficiency, and robustness. Various technologies such as Python, MySQL, Firebase, and others were employed to enhance the adaptability of the software. The results demonstrate the effectiveness of the software, with a continuously growing self-adaptation rate and improvements in response times. Probability models were applied to analyze the software’s effectiveness in fault situations. The implementation of virtual cables and multiprocessing significantly improved performance, achieving higher execution speed and scalability. In summary, this study presents self-adaptive software that rectifies errors, optimizes performance, and maintains functionality in the presence of faults, contributing to efficiency in Internet task automation. Full article
(This article belongs to the Special Issue Advances and Applications of Complex Data Analysis and Computing)
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Review

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7 pages, 2075 KiB  
Review
Biomedical Flat and Nested Named Entity Recognition: Methods, Challenges, and Advances
by Yesol Park, Gyujin Son and Mina Rho
Appl. Sci. 2024, 14(20), 9302; https://doi.org/10.3390/app14209302 - 12 Oct 2024
Viewed by 727
Abstract
Biomedical named entity recognition (BioNER) aims to identify and classify biomedical entities (i.e., diseases, chemicals, and genes) from text into predefined classes. This process serves as an important initial step in extracting biomedical information from textual sources. Considering the structure of the entities [...] Read more.
Biomedical named entity recognition (BioNER) aims to identify and classify biomedical entities (i.e., diseases, chemicals, and genes) from text into predefined classes. This process serves as an important initial step in extracting biomedical information from textual sources. Considering the structure of the entities it addresses, BioNER tasks are divided into two categories: flat NER, where entities are non-overlapping, and nested NER, which identifies entities embedded within another. While early studies primarily addressed flat NER, recent advances in neural models have enabled more sophisticated approaches to nested NER, gaining increasing relevance in the biomedical field, where entity relationships are often complex and hierarchically structured. This review, thus, focuses on the latest progress in large-scale pre-trained language model-based approaches, which have shown the significantly improved performance of NER. The state-of-the-art flat NER models have achieved average F1-scores of 84% on BC2GM, 89% on NCBI Disease, and 92% on BC4CHEM, while nested NER models have reached 80% on the GENIA dataset, indicating room for enhancement. In addition, we discuss persistent challenges, including inconsistencies of named entities annotated across different corpora and the limited availability of named entities of various entity types, particularly for multi-type or nested NER. To the best of our knowledge, this paper is the first comprehensive review of pre-trained language model-based flat and nested BioNER models, providing a categorical analysis among the methods and related challenges for future research and development in the field. Full article
(This article belongs to the Special Issue Advances and Applications of Complex Data Analysis and Computing)
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40 pages, 2584 KiB  
Review
Bias in Machine Learning: A Literature Review
by Konstantinos Mavrogiorgos, Athanasios Kiourtis, Argyro Mavrogiorgou, Andreas Menychtas and Dimosthenis Kyriazis
Appl. Sci. 2024, 14(19), 8860; https://doi.org/10.3390/app14198860 - 2 Oct 2024
Viewed by 2078
Abstract
Bias could be defined as the tendency to be in favor or against a person or a group, thus promoting unfairness. In computer science, bias is called algorithmic or artificial intelligence (i.e., AI) and can be described as the tendency to showcase recurrent [...] Read more.
Bias could be defined as the tendency to be in favor or against a person or a group, thus promoting unfairness. In computer science, bias is called algorithmic or artificial intelligence (i.e., AI) and can be described as the tendency to showcase recurrent errors in a computer system, which result in “unfair” outcomes. Bias in the “outside world” and algorithmic bias are interconnected since many types of algorithmic bias originate from external factors. The enormous variety of different types of AI biases that have been identified in diverse domains highlights the need for classifying the said types of AI bias and providing a detailed overview of ways to identify and mitigate them. The different types of algorithmic bias that exist could be divided into categories based on the origin of the bias, since bias can occur during the different stages of the Machine Learning (i.e., ML) lifecycle. This manuscript is a literature study that provides a detailed survey regarding the different categories of bias and the corresponding approaches that have been proposed to identify and mitigate them. This study not only provides ready-to-use algorithms for identifying and mitigating bias, but also enhances the empirical knowledge of ML engineers to identify bias based on the similarity that their use cases have to other approaches that are presented in this manuscript. Based on the findings of this study, it is observed that some types of AI bias are better covered in the literature, both in terms of identification and mitigation, whilst others need to be studied more. The overall contribution of this research work is to provide a useful guideline for the identification and mitigation of bias that can be utilized by ML engineers and everyone who is interested in developing, evaluating and/or utilizing ML models. Full article
(This article belongs to the Special Issue Advances and Applications of Complex Data Analysis and Computing)
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